Systems and methods for a multi-tier self-organizing network architecture

ABSTRACT

A middle-tier self-organizing network (mSON) may obtain configuration parameters concerning a cluster, one or more operating profiles, and one or more usage schedules respectively associated with the one or more operating profiles, from a centralized self-organizing network (cSON). The mSON may identify a distributed self-organizing network (dSON) of one or more dSONs associated with the cluster and may select, based on identifying the dSON, an operating profile, of the one or more operating profiles, and a respective usage schedule of the one or more usage schedules. The mSON may generate, based on the configuration parameters, monitoring information, and may send the operating profile, the usage schedule, and the monitoring information to the dSON.

BACKGROUND

Self-organizing networks (SONs) may be deployed to automatically managefunctions of wireless telecommunications networks, such as 4G and 5Gwireless networks. For example, SON functions may be used to improvecoverage or capacity of a wireless telecommunications network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1F are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

FIGS. 4-6 are flow charts of example processes concerning a multi-tierself-organizing network architecture.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

A wireless telecommunications network may utilize one or moretechnologies to automate functions of the wireless telecommunicationsnetwork. For example, a wireless telecommunications network may utilizeself-organizing network (SON) technology to plan, configure, manage,and/or optimize functions of the wireless telecommunications network. Insome cases, the wireless telecommunications network may use acentralized self-organizing network (cSON) (e.g., associated with abackhaul portion of the wireless telecommunications network) and one ormore distributed self-organizing networks (dSONs) (e.g., associated witha fronthaul portion of the wireless telecommunications network). ThecSON may obtain information regarding performance of the wirelesstelecommunications network and generate operating profiles to optimizeperformance of different elements of the wireless telecommunicationsnetwork. The cSON may send an operating profile to a dSON, which mayapply the operating profile to control functionality of a base stationof the wireless telecommunications network. The dSON may be able toreact to real-time, practical conditions experienced by the base station(e.g., by adjusting base station settings), but functionality of thedSON is limited by parameters set forth in the operating profile.Moreover, in some cases, the dSON and cSON only communicate atparticular times, which prevents the dSON from obtaining an updatedoperating profile to address short-term base station performancechanges.

Some implementations described herein provide a multi-tierself-organizing network architecture that includes a cSON, one or moredSONs, and a middle-tier self-organizing network (mSON) (e.g.,associated with a midhaul portion of a wireless telecommunicationsnetwork). In some implementations, the cSON may generate, based onconfiguration parameters of a cluster of the wireless telecommunicationsnetwork, one or more operating profiles and one or more respective usageschedules. In some implementations, the cSON may send the configurationparameters, the one or more operating profiles, and the one or morerespective usage schedules to the mSON. In some implementations, themSON may identify the dSON and may select and send an operating profileand a respective usage schedule to the dSON. In some implementations,the dSON may apply the operating profile according to the respectiveusage schedule to control operation of a base station associated withthe dSON. In some implementations, the dSON may monitor at least oneperformance indicator of the base station and may send performanceinformation to the mSON. In some implementations, the mSON may select adifferent operating profile and a respective different usage scheduleand may send the different operating profile and the different usageschedule to the dSON (e.g., for the dSON to control operation of thebase station).

In this way, some implementations described herein enable the dSON toobtain an updated operating profile (e.g., that may optimize aperformance of the base station associated with the dSON) based onsending performance information to the mSON. In some implementations,the mSON may communicate with the dSON on a more frequent basis than thedSON would otherwise communicate with the cSON, and/or a latency (e.g.,an end-to-end communications transmission time) associated withcommunicating between the dSON and the mSON may be lower than a latencyassociated with communicating between the dSON and the cSON (e.g.,because the dSON and the mSON may be physically closer to each otherthan the dSON and the cSON). This may allow the dSON to quickly obtainand apply the updated operating profile, which may mitigate aninefficient use of base station resources that would otherwise occur ina SON architecture without an mSON.

Moreover, in some implementations, the mSON may provide reactivefunctionality (e.g., to performance information obtained from the dSON)that the cSON cannot otherwise provide. Further, the mSON may provideprocessing functionality (e.g., to select an appropriate operatingprofile for the dSON) that the dSON cannot otherwise provide (e.g.,because of limited dSON computing resources). In this way, the mSON mayincrease a likelihood of an optimal use of resources of the base stationand/or of the wireless telecommunications network while allowing thedSON to focus on controlling and monitoring operation of the basestation and allowing the cSON to focus on processing large amounts ofperformance information to generate new operating profiles.

FIGS. 1A-1F are diagrams of one or more example implementations 100described herein. Example implementation(s) 100 may include one or morebase stations 102, one or more distributed self-organizing networks(dSONs) 104, a middle-tier self-organizing network (mSON) mSON 106, anda centralized self-organizing network (cSON) 108. The one or more basestations 102, the one or more dSONs 104, the mSON 106, and the cSON 108may be associated with a wireless telecommunications network. Forexample, a base station 102 and a dSON 104 may be associated with afronthaul portion of the wireless telecommunications network (e.g., thedSON 104 may be located at and/or associated with the base station 102and/or a baseband distributed unit), the mSON 106 may be associated witha midhaul and/or backhaul portion of the wireless telecommunicationsnetwork (e.g., the mSON 106 may be located at and/or associated with abaseband centralized unit), and/or the cSON 108 may be associated with atransport portion of the wireless telecommunications network (e.g., thecSON 108 may be located at and/or associated with a network managementsystem).

As shown in FIG. 1B and by reference number 110, the cSON 108 maydetermine and/or identify at least one cluster of the wirelesstelecommunications network. A cluster may include one or more dSONs 104and one or more base stations 102 (e.g., a cluster may be a segment ofthe fronthaul portion of the wireless telecommunications network). Forexample, a cluster may include one or more dSONs 104 and one or morebase stations 102 associated with an area (e.g., an area associated withone or more city square blocks, an area associated with one or moresquare miles or square kilometers, and/or the like).

As shown by reference number 112, the cSON 108 may determineconfiguration parameters concerning the at least one cluster. Theconfiguration parameters may set and/or define requirements for the atleast one cluster (e.g., for all or a set of the one or more dSONs 104and the one or more base stations 102 of the at least one cluster, forall or a set of user devices that connect to the at least one cluster,and/or the like). For example, the configuration parameters may includea data rate requirement (e.g., minimum and/or maximum download andupload bitrates supported by the one or more dSONs 104 and the one ormore base stations 102); a density requirement (e.g., a number ofcommunication sessions (e.g., with user devices) supported by the one ormore dSONs 104 and the one or more base stations 102; a latencyperformance requirement (e.g., an end-to-end communications transmissiontime requirement supported by the one or more dSONs 104 and the one ormore base stations 102); a mobility requirement (e.g., a speed range oftraveling user devices supported by the one or more dSONs 104 and theone or more base stations 102); and/or the like).

As shown by reference number 114, the cSON 108 may generate one or moreoperating profiles and/or one or more respective usage schedules. Eachof the operating profiles may have characteristics to support aparticular type, or types, of communication. For example, an operatingprofile may be an enhanced mobile broad band profile (e.g., forproviding enhanced broadband access in dense areas, ultra-high bandwidthaccess in dense areas, broadband access in public transport systems,and/or the like); a massive machine-type communication profile (e.g.,for providing automatic data generation, exchange, processing, andactuation among intelligent machines); a connected vehicles profile(e.g., for providing vehicle-to-everything (V2X) communications, such asvehicle-to-vehicle (V2V) communications, vehicle-to-infrastructure (V2I)communications, vehicle-to-network (V2N) communications, andvehicle-to-pedestrian (V2P) communications, and/or the like); anenhanced multi-media profile (e.g., for providing broadcast services, ondemand and live TV, mobile TV, augmented reality (AR), virtual reality(VR), and/or the like); an internet of things profile (e.g., forproviding metering, lighting management in buildings and cities,environmental monitoring, traffic control, and/or the like); anultra-reliable low latency communication profile (e.g., for providingprocess automation, automated factories, tactile interaction, emergencycommunications, urgent healthcare, and/or the like); a fixed wirelessaccess profile (e.g., for providing localized network access and/or thelike); and/or the like. Furthermore, the one or more operating profilesmay include numerous variations of each kind of operating profile. Forexample, the one or more operating profiles may include a firstconnected vehicles profile (e.g., to support V2X communications), asecond connected vehicles profile (e.g., to support V2V communications),a third connected vehicles profile (e.g., to support V2Icommunications), a fourth connected vehicles profile (e.g., to supportV2N communications), a fifth connected vehicles profile (e.g., tosupport V2P communications), and/or the like. The one or more respectiveusage schedules may indicate when (e.g., a period of time) the one ormore or more operating profiles are to be applied (e.g., by a dSON 104in the at least one cluster).

In some implementations, the cSON 108 may generate the one or moreoperating profiles and/or the one or more respective usage schedulesbased on the configuration parameters. For example, when theconfiguration parameters include a high data rate requirement (e.g.,download and upload speeds for a user device greater than a threshold,such as 50 Mbps), the cSON 108 may generate an enhanced mobile broadband profile, an enhanced multi-media profile, and/or the like. Inanother example, when the configuration parameters include a low latencyperformance requirement (e.g., an end-to-end communications transmissiontime for a user device less than or equal to a threshold, such as about10 ms), the cSON 108 may generate an ultra-reliable low latencycommunication profile, a massive machine-type communication profile,and/or the like. The cSON 108 may generate the one or more operatingprofiles and/or the one or more respective usage schedules to optimizespectral assets of a base station (e.g., base station 102), a cluster,and/or the wireless telecommunications network.

In some implementations, the cSON 108 may generate the one or moreoperating profiles and/or the one or more respective usage schedulesusing a machine learning model. In some implementations, the cSON 108may generate and/or train the machine learning model to generate anoperating profile and/or a usage schedule for the operating profile. Forexample, the cSON 108 may obtain (e.g., from a data structure) andprocess historical information (e.g., historical information concerningone or more operating profiles and/or one or more respective usageschedules); historical performance information concerning one or moredSONs 104 (e.g., historical performance information concerning each dSON104 that has applied an operating profile (e.g., according to arespective usage schedule)); historical performance informationconcerning one or more base stations 102 (e.g., historical informationconcerning each base station 102 controlled by a dSON 104 that hasapplied an operating profile (e.g., according to a respective usageschedule)); and/or the like to generate an operating profile and/or ausage schedule for the operating profile.

In some implementations, the cSON 108 may perform a set of datamanipulation procedures to pre-process the historical information togenerate the machine learning model. The cSON 108 may use a datapre-processing procedure, a model training procedure, a modelverification procedure, and/or the like to pre-process the historicalinformation to generate pre-processed historical information. Forexample, the cSON 108 may pre-process the historical information toremove irrelevant information, confidential data, corrupt data, and/orthe like. In this way, the cSON 108 may organize thousands, millions, orbillions of data entries for machine learning and model generation.

In some implementations, the cSON 108 may perform a training operationwhen generating the machine learning model. For example, the cSON 108may portion the historical information into a training set (e.g., a setof data to train the model), a validation set (e.g., a set of data usedto evaluate a fit of the model and/or to fine tune the model), a testset (e.g., a set of data used to evaluate a final fit of the model),and/or the like. In some implementations, a minimum feature set may becreated from pre-processing and/or dimensionality reduction of thehistorical information. In some implementations, the cSON 108 may trainthe machine learning model on this minimum feature set, thereby reducingprocessing required to train the machine learning model, and may apply aclassification technique to the minimum feature set.

When training the machine learning model, the cSON 108 may utilize arandom forest classifier technique to train the machine learning model.For example, the cSON 108 may utilize a random forest classifiertechnique to construct multiple decision trees during training and mayoutput a classification of the historical information. As anotherexample, the cSON 108 may utilize a random forest regression techniqueto construct multiple decision trees during training and may output anumeric predication associated with the historical information.Additionally, or alternatively, when training the machine learningmodel, the cSON 108 may utilize one or more gradient boosting techniquesto generate the machine learning model. For example, the cSON 108 mayutilize an xgboost classifier technique, an xgboost regressiontechnique, a gradient boosting machine (GBM) technique, a gradientboosting tree, and/or the like to generate a prediction model from a setof weak prediction models.

When training the machine learning model, the cSON 108 may utilize alogistic regression technique to train the machine learning model. Forexample, the cSON 108 may utilize a binary classification of thehistorical information (e.g., whether the historical information isindicative of a particular accurate prediction), a multi-classclassification of the historical information (e.g., whether thehistorical information is indicative of one or more accuratepredictions), and/or the like to train the machine learning model.Additionally, or alternatively, when training the machine learningmodel, the cSON 108 may utilize a naïve Bayes classifier technique totrain the machine learning model. For example, the behavioral analyticsplatform may utilize binary recursive partitioning to divide thehistorical information into various binary categories (e.g., startingwith whether the historical information is indicative of a particularaccurate prediction). Based on using recursive partitioning, the cSON108 may reduce utilization of computing resources relative to manual,linear sorting and analysis of data points, thereby enabling use ofthousands, millions, or billions of data points to train a machinelearning model, which may result in a more accurate machine learningmodel than using fewer data points.

Additionally, or alternatively, when training the machine learningmodel, the cSON 108 may utilize a support vector machine (SVM)classifier technique. For example, the cSON 108 may utilize a linearmodel to implement non-linear class boundaries, such as via a max marginhyperplane. Additionally, or alternatively, when utilizing the SVMclassifier technique, the cSON 108 may utilize a binary classifier toperform a multi-class classification. Use of an SVM classifier techniquemay reduce or eliminate overfitting, may increase a robustness of themachine learning model to noise, and/or the like.

In some implementations, the cSON 108 may train the machine learningmodel using a supervised training procedure. In some implementations,the cSON 108 may receive additional input to the machine learning modelfrom other sources. In some implementations, the cSON 108 may use one ormore other model training techniques, such as a neural networktechnique, and/or the like. For example, the cSON 108 may perform amulti-layer artificial neural network processing technique (e. g, usinga recurrent neural network architecture, a two-layer feedforward neuralnetwork architecture, a three-layer feedforward neural networkarchitecture, and/or the like) to perform pattern recognition withregard to patterns in the historical information. Use of artificialneural network processing technique may improve an accuracy of asupervised learning model generated by the cSON 108 by being more robustto noisy, imprecise, or incomplete data, and by enabling the cSON 108 todetect patterns and/or trends undetectable to human analysts or systemsusing less complex techniques. Furthermore, when using a recurrentneural network architecture, long short-term memory (LSTM) may beemployed to classify, make predictions, and/or otherwise processtime-series data, which may be useful to predict how patterns changeover time (e.g., over a month, a year, and/or the like).

In some implementations, a different device, such as a server device,may generate and/or train the machine learning model. The cSON 108 mayobtain the machine learning model from the different device. Forexample, the different device may send the machine learning model to thecSON 108 and/or the cSON 108 may request and receive the machinelearning model from the different device. In some implementations, thedifferent device may update and send (e.g., on a scheduled basis, on anon-demand basis, on a triggered basis, and/or the like) the machinelearning model to the cSON 108. The cSON 108 may obtain the updatedmachine learning model from the different device.

In this way, the cSON 108 may use artificial intelligence techniques,machine learning techniques, deep learning techniques, and/or the liketo determine one or more associations between historical information anda determination indicating an operating profile and a usage schedule forthe operating profile.

As shown by reference number 116, the cSON 108 may send theconfiguration parameters, the one or more operating profiles, and/or theone or more respective usage schedules to the mSON 106.

As shown in FIG. 1C and by reference number 118, the mSON 106 mayidentify a dSON 104, of the one or more dSONs 104, associated with theat least one cluster. For example, the configuration parameters mayinclude information indicating the one or more dSONs 104 and/or one ormore base stations 102 that are included in the at least one cluster,and the mSON 106 may process the configuration parameters to identifythe dSON 104.

As shown by reference number 120, the mSON 106 may select, based onidentifying the dSON, an operating profile, of the one or more operatingprofiles, and/or a respective usage schedule of the one or more usageschedules (e.g., an operating profile to be applied by the dSON 104according to the respective usage schedule). In some implementations,the mSON 106 may obtain capability information concerning at least onecapability (e.g., a data rate capability, a density capability, alatency performance capability, a mobility capability, and/or the like)of a base station 102 associated with the dSON 104, and may select theoperating profile and/or the respective usage schedule based on thecapability information. For example, the mSON 106 may select an enhancedmobile broad band profile when the base station 102 has a high data ratecapability, may select an ultra-reliable low latency communicationprofile when the base station 102 has a low latency performancecapability, may select a massive machine-type communication profile whenthe base station 102 has a high connection density capability, and/orthe like.

Additionally, or alternatively, the mSON 106 may obtain informationconcerning at least one performance indicator of the base station 102(e.g., obtain real-time information concerning a performance of the basestation 102) and may select the operating profile and/or the respectiveusage schedule based on the at least one performance indicator. The atleast one performance indicator may include a data rate indicator (e.g.,download and upload bitrates provided by the base station 102); aconnection density indicator (e.g., a number of communication sessionsprovided by the base station 102); a latency indicator (e.g., anend-to-end communications transmission time provided by the base station102); a reliability indicator (e.g., a percentage of packetssuccessfully transmitted between a user device and a network via thebase station 102); and/or the like.

As shown by reference number 122, the mSON 106 may generate, based onthe configuration parameters, monitoring information. The monitoringinformation may identify at least one performance indicator, of the oneor more performance indicators, of the base station 102 that the dSON104 is to monitor (e.g., to determine a performance of the base station102). The monitoring information may indicate criteria for monitoringthe at least one performance indicator.

As shown by reference number 124, the mSON 106 may send the operatingprofile, the respective usage schedule, and/or the monitoringinformation to the dSON 104.

As shown in FIG. 1D and by reference number 126, the dSON 104 may applythe operating profile (e.g., to control operation of the base station102 associated with the dSON 104). In some implementations (e.g., whenthe mSON 106 sends the operating profile but not the respective usageschedule), the dSON 104 may cause the base station 102 to operateaccording to the operating profile upon receiving the operating profile.Additionally, or alternatively (e.g., when the mSON 106 sends theoperating profile and the respective usage schedule), the dSON 104 mayapply the operating profile according to the respective usage schedule.For example, the dSON 104 may determine, based on the respective usageschedule, a period of time in which to apply the operating profile andmay cause the base station 102 to operate according to the operatingprofile during the period of time.

As shown by reference number 128, the dSON 104 may monitor at least oneperformance indicator of the base station 102 (e.g., based on themonitoring information). As shown by reference number 130, the dSON 104may generate performance information based on monitoring the at leastone performance indicator. In some implementations, the dSON 104 mayidentify, based on the monitoring information, a threshold associatedwith the at least one performance indicator and may determine whetherthe at least one performance indicator satisfies the threshold.Accordingly, the dSON 104 may generate performance information thatindicates a number of times that the at least one performance indicatorsatisfied the threshold (e.g., within a particular period of time), anamount of time that the at least one performance indicator satisfied thethreshold, and/or the like.

For example, the monitoring information may indicate that the dSON 104is to determine whether a connection density indicator of the basestation 102 satisfies (e.g., is greater than or equal to) a connectiondensity threshold (e.g., a number of communication sessions that thebase station 102 can provide while maintaining consistent quality ofservice (QoS)). Accordingly, based on monitoring the connection densityindicator, the dSON 104 may generate performance information thatindicates a number of times that the connection density threshold wassatisfied and an amount of time that the connection density thresholdwas satisfied.

As shown in FIG. 1E and by reference number 132, the dSON 104 may sendthe performance information to the mSON 106. The dSON 104 may send theperformance information on a triggered basis (e.g., when the at leastone performance indicator satisfies the threshold), on a scheduled basis(e.g., every minute, every 5 minutes, every hour, every day, and/or thelike), on an on-demand basis (e.g., based on receiving a request for theperformance information from the mSON 106), and/or the like.

In some implementations, the mSON 106 may process the performanceinformation to determine a performance of the base station 102associated with the dSON 104. The mSON 106 may determine, based on theperformance of the base station 102, that the base station 102 is notoperating optimally. Accordingly, the mSON 106 may cause the dSON 104 toshut down (e.g., to conserve resources of the dSON 104). Additionally,or alternatively, as shown by reference number 134, the mSON 106 mayselect a different operating profile and/or a respective different usageschedule (e.g., to be applied by the dSON 104 to improve the performanceof the base station 102). For example, the mSON 106 may determine, basedon the performance of the base station 102, that the base station 102 isprimarily supporting communication sessions for IoT devices, rather thanmedia consuming user devices. The mSON 106 may therefore determine thatan enhanced mobile broad band profile is no longer appropriate and mayselect an internet of things profile and/or an associated usageschedule. In another example, the mSON 106 may determine, based on theperformance of the base station 102, that the base station 102 isprimarily supporting high mobility user devices (e.g., where a userdevice is moving at a rate of speed that satisfies (e.g., is greaterthan an or equal to) a threshold, such as 40 miles per hour (e.g., whichmay indicate that the user device is associated with travel on ahighway)) and may select a high mobility profile and/or an associatedusage schedule. The mSON 106 may select the high mobility profile from aset of mobility profiles (e.g., that may include a low mobility profile(e.g., for user devices moving at a low rate of speed (e.g., less than10 miles per hour)), a moderate mobility profile (e.g., for user devicesmoving at a moderate rate of speed (e.g., greater than or equal to 10miles per hour and/or less than 40 miles per hour)), a high mobilityprofile (e.g., for user device moving at a high rate of speed (e.g.,greater than or equal to 40 miles per hour)), and/or the like) that weresent as part of the one or more operating profiles by the cSON 108 tothe mSON 106 (e.g., as described herein in relation to reference number116).

Additionally, or alternatively, the mSON 106 may receive, from one ormore additional dSONs 104 in the at least one cluster, additionalperformance information concerning the one or more additional dSONs 104.The mSON 106 may determine a performance of the cluster based on theperformance information concerning the dSON 104 and the additionalperformance information concerning the one or more additional dSONs 104and may select, based on the performance of the cluster, a differentoperating profile, of the one or more operating profiles, and/or arespective different usage schedule of the one or more usage schedules.For example, the mSON 106 may determine, based on the performance of thecluster, that the cluster is not providing enough support for massivemachine-type communications and may select a massive machine-typecommunication profile and/or an associated usage schedule.

As shown by reference number 136, the mSON 106 may send the differentoperating profile and/or the respective different usage schedule to thedSON 104. As shown by reference number 138, the dSON 104 may apply thedifferent operating profile in a similar manner as described herein inrelation to FIG. 1D. For example, the dSON 104 may apply the differentoperating profile upon receiving the operating profile. As anotherexample, the dSON 104 may apply the different operating profileaccording to the respective different usage schedule. In someimplementations, the dSON 104 may monitor, after applying the differentprofile, the at least one performance indicator of the base station 102based on the monitoring information, and may generate additionalperformance information in a similar manner as described herein inrelation to FIG. 1D. In some implementations, the dSON 104 may send theadditional performance information to the mSON 106 and the mSON 106 mayprocess the additional performance information in a similar manner asdescribed herein.

As shown in FIG. 1F and by reference number 140, the mSON 106 may sendthe performance information to the cSON 108. As shown by referencenumber 142, the cSON 108 may process the performance information and maycause one or more actions to be performed. For example, cSON 108 mayupdate the machine learning model (e.g., that generates the one or moreoperating profiles and the one or more respective usage schedules) basedon the performance information.

In another example, the cSON 108 may generate one or more instructionsto control at least one antenna of the base station 102 (e.g., the basestation 102 associated with the dSON 104). The one or more instructionsmay include a set of instructions to control a tilt angle of the atleast one antenna. The one or more instructions may also include a setof instructions for the mSON 106 to determine a beam shape and/or a beamdirection of at least one beam associated with the at least one antenna.The cSON 108 may send the one or more instructions to the mSON 106,which may process the one or more instructions and/or the performanceinformation (e.g., of the base station 102 associated with the dSON 104)to determine a set of instructions to control the beam shape and/or thebeam direction of the at least one beam associated with the at least oneantenna. The mSON 106 may send the set of instructions to control thetilt angle of the at least one antenna and/or the set of instructions tocontrol the beam shape and/or the beam direction of the at least onebeam associated with the at least one antenna to the dSON 104, which,when executed by the dSON 104, may cause the tilt angle of the at leastone antenna to adjust and/or the beam shape and/or the beam direction ofthe at least one beam to adjust.

The number and arrangement of devices and networks shown in FIGS. 1A-1Fare provided as one or more examples. In practice, there may beadditional devices and/or networks, fewer devices and/or networks,different devices and/or networks, or differently arranged devicesand/or networks than those shown in FIGS. 1A-1F. Furthermore, two ormore devices shown in FIGS. 1A-1F may be implemented within a singledevice, or a single device shown in FIGS. 1A-1F may be implemented asmultiple, distributed devices. Additionally, or alternatively, a set ofdevices (e.g., one or more devices) of example implementation 100 mayperform one or more functions described as being performed by anotherset of devices of example implementation 100.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods described herein may be implemented. As shown in FIG. 2,environment 200 may include a base station 210, a dSON 220, an mSON 230,and a cSON 240. Devices of environment 200 may interconnect via wiredconnections, wireless connections, optical communications or acombination of wired, optical, and wireless connections. Someimplementations may be performed in association with a wirelesstelecommunications network, such as a third generation (3G) network, afourth generation (4G) network, a long term evolution (LTE) network, afifth generation (5G) network, and/or the like.

Base station 210 includes one or more devices capable of communicatingwith one or more user devices (not shown) using a Radio AccessTechnology (RAT). For example, base station 210 may include a basetransceiver station, a radio base station, a node B, an evolved node B(eNB), a gNB, a base station subsystem, a cellular site, a cellulartower (e.g., a cell phone tower, a mobile phone tower), an access point,a transmit receive point (TRP), a radio access node, a macrocell basestation, a microcell base station, a picocell base station, a femtocellbase station, or a similar type of device. Base station 210 may transfertraffic between a user device (e.g., using a cellular RAT), other basestations 210 (e.g., using a wireless interface or a backhaul interface,such as a wired backhaul interface), and/or a network. Base station 210may provide one or more cells that cover geographic areas. Some basestations 210 may be mobile base stations. Some base stations 210 may becapable of communicating using multiple RATs.

In some implementations, base station 210 may perform scheduling and/orresource management for user devices covered by base station 210 (e.g.,user devices covered by a cell provided by base station 210). In someimplementations, base stations 210 may be controlled or coordinated by anetwork controller, which may perform load balancing, network-levelconfiguration, and/or the like. The network controller may communicatewith base stations 210 via a wireless, optical, or wireline backhaul. Insome implementations, base station 210 may include a network controller,a self-organizing network (SON) module or component (e.g., dSON 220), ora similar module or component. Base station 210 may be associated with afronthaul portion of the wireless telecommunications network.

dSON 220 is implemented by one or more devices (e.g., a server device, agroup of server devices, a desktop computer, a laptop computer, and/or asimilar type of device). In some implementations, dSON 220 may be asoftware implementation of a machine (e.g., a computer) that executesprograms like a physical machine. dSON 220 may be either a systemvirtual machine or a process virtual machine, depending upon use anddegree of correspondence to any real machine by dSON 220. A systemvirtual machine may provide a complete system platform that supportsexecution of a complete operating system. A process virtual machine mayexecute a single program, and may support a single process. In someimplementations, dSON 220 may be implemented by one or more devices thatinclude a communication interface that allows dSON 220 to receiveinformation from and/or transmit information to mSON 230. dSON 220 maybe associated with a fronthaul portion of the wirelesstelecommunications network.

mSON 230 is implemented by one or more devices (e.g., a server device, agroup of server devices, a desktop computer, a laptop computer, and/or asimilar type of device). In some implementations, mSON 230 may be asoftware implementation of a machine (e.g., a computer) that executesprograms like a physical machine. mSON 230 may be either a systemvirtual machine or a process virtual machine, depending upon use anddegree of correspondence to any real machine by mSON 230. In someimplementations, mSON 230 may be implemented by one or more devices thatinclude a communication interface that allows mSON 230 to receiveinformation from and/or transmit information to dSON 220 and/or cSON240. mSON 230 may be associated with a midhaul portion of the wirelesstelecommunications network.

cSON 240 is implemented by one or more devices (e.g., a server device, agroup of server devices, a desktop computer, a laptop computer, and/or asimilar type of device). In some implementations, cSON 240 may be asoftware implementation of a machine (e.g., a computer) that executesprograms like a physical machine. cSON 240 may be either a systemvirtual machine or a process virtual machine, depending upon use anddegree of correspondence to any real machine by cSON 240. In someimplementations, cSON 240 may be implemented by one or more devices thatinclude a communication interface that allows cSON 240 to receiveinformation from and/or transmit information to mSON 230. cSON 240 maybe associated with a backhaul portion of the wireless telecommunicationsnetwork.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as one or more examples. In practice, there may be additionaldevices and/or networks, fewer devices and/or networks, differentdevices and/or networks, or differently arranged devices and/or networksthan those shown in FIG. 2. Furthermore, two or more devices shown inFIG. 2 may be implemented within a single device, or a single deviceshown in FIG. 2 may be implemented as multiple, distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of environment 200 may perform one or more functions describedas being performed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to base station 210, dSON 220, mSON 230, and/or cSON 240.In some implementations base station 210, dSON 220, mSON 230, and/orcSON 240 may include one or more devices 300 and/or one or morecomponents of device 300. As shown in FIG. 3, device 300 may include abus 310, a processor 320, a memory 330, a storage component 340, aninput component 350, an output component 360, and a communicationinterface 370.

Bus 310 includes a component that permits communication among multiplecomponents of device 300. Processor 320 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 320is a central processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 360 includes a component thatprovides output information from device 300 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 300 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 370 may permit device300 to receive information from another device and/or provideinformation to another device. For example, communication interface 370may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a wireless local area networkinterface, a cellular network interface, and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. As used herein,the term “computer-readable medium” refers to a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 concerning a multi-tierself-organizing network architecture. In some implementations, one ormore process blocks of FIG. 4 may be performed by an mSON (e.g., mSON106 and/or mSON 230). In some implementations, one or more processblocks of FIG. 4 may be performed by another device or a group ofdevices separate from or including the mSON, such as a basebandcentralized unit of a wireless telecommunications network, and/or thelike.

As shown in FIG. 4, process 400 may include obtaining configurationparameters concerning a cluster, one or more operating profiles, and oneor more usage schedules respectively associated with the one or moreoperating profiles, from a cSON (block 410). For example, the mSON(e.g., using processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370, and/orthe like) may obtain configuration parameters concerning a cluster, oneor more operating profiles, and one or more usage schedules respectivelyassociated with the one or more operating profiles, from a cSON, asdescribed above.

As further shown in FIG. 4, process 400 may include identifying a dSONof one or more dSONs associated with the cluster (block 420). Forexample, the mSON (e.g., using processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370, and/or the like) may identify a dSON of one or more dSONsassociated with the cluster, as described above.

As further shown in FIG. 4, process 400 may include selecting, based onidentifying the dSON, an operating profile of the one or more operatingprofiles, and a respective usage schedule of the one or more usageschedules (block 430). For example, the mSON (e.g., using processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370, and/or the like) may select, based onidentifying the dSON, an operating profile of the one or more operatingprofiles, and a respective usage schedule of the one or more usageschedules, as described above. When selecting the operating profile andthe respective usage schedule, the mSON may obtain informationconcerning at least one performance indicator of a base stationassociated with the dSON; and may select the operating profile and therespective usage schedule based on the at least one performanceindicator. The at least one performance indicator may be a data rateindicator, a connection density indicator, a latency indicator, areliability indicator, and/or the like.

As further shown in FIG. 4, process 400 may include generating, based onthe configuration parameters, monitoring information (block 440). Forexample, the mSON (e.g., using processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370, and/or the like) may generate, based on the configurationparameters, monitoring information, as described above. When generatingthe monitoring information, the mSON may identify a base stationassociated with the dSON; may determine at least one capability of thebase station; and may generate, based on the configuration parameters,monitoring instructions that concern the at least one capability. Insome implementations, the monitoring information may identify one ormore performance indicators of a base station associated with monitoringthe dSON.

As further shown in FIG. 4, process 400 may include sending theoperating profile, the usage schedule, and the monitoring information tothe dSON (block 450). For example, the mSON (e.g., using processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370, and/or the like) may send theoperating profile, the usage schedule, and the monitoring information tothe dSON, as described above.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

For example, the mSON may receive, from the dSON performance informationconcerning the dSON. In some implementations, the mSON may cause, basedon the performance information concerning the dSON, the dSON to shutdown (e.g., for a period of time). In some implementations, the mSON mayselect, based on the performance information, a different operatingprofile, of the one or more operating profiles, and a respectivedifferent usage schedule of the one or more usage schedules; and maysend the selected operating profile and the selected usage schedule tothe dSON. The mSON may send the performance information to the cSON topermit the cSON to update a machine learning model associated withgenerating operating profiles and usage schedules.

In some implementations, the mSON may receive, from the dSON, aftersending the operating profile, the usage schedule, and the monitoringinformation to the dSON, performance information concerning the dSON;may receive, from one or more additional dSONs in the cluster,additional performance information concerning the one or more additionaldSONs; may determine a performance of the cluster based on theperformance information concerning the dSON and the additionalperformance information concerning the one or more additional dSONs; mayselect, based on the performance of the cluster, a different operatingprofile, of the one or more operating profiles, and a respectivedifferent usage schedule of the one or more usage schedules; and maysend the different operating profile and the different usage schedule tothe dSON.

In some implementations, an end-to-end transmission time forcommunications between the dSON and the mSON is less than an end-to-endtransmission time for communications between the dSON and the cSON.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 concerning a multi-tierself-organizing network architecture. In some implementations, one ormore process blocks of FIG. 5 may be performed by a dSON (e.g., dSON 104and/or dSON 220). In some implementations, one or more process blocks ofFIG. 5 may be performed by another device or a group of devices separatefrom or including the dSON, such as a baseband distributed unit of awireless telecommunications network, and/or the like.

As shown in FIG. 5, process 500 may include obtaining an operatingprofile, a usage schedule, and monitoring information from an mSON(block 510). For example, the dSON (e.g., using processor 320, memory330, storage component 340, input component 350, output component 360,communication interface 370, and/or the like) may obtain an operatingprofile, a usage schedule, and monitoring information from an mSON, asdescribed above. The operating profile may be an enhanced mobile broadband profile, a massive machine-type communication profile, a connectedvehicles profile, an enhanced multi-media profile, an internet of thingsprofile, an ultra-reliable low latency communication profile, a fixedwireless access profile, and/or the like.

As further shown in FIG. 5, process 500 may include applying theoperating profile according to the usage schedule to control operationof a base station (block 520). For example, the dSON (e.g., usingprocessor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370, and/or the like) mayapply the operating profile according to the usage schedule to controloperation of a base station as described above. The base station may beassociated with the dSON. When applying the operating profile accordingto the usage schedule, the dSON may determine, based on the usageschedule, a period of time in which to apply the operating profile andmay cause the base station to operate according to the operating profileduring the period of time.

As further shown in FIG. 5, process 500 may include monitoring, based onthe monitoring information, at least one performance indicator of thebase station (block 530). For example, the dSON (e.g., using processor320, memory 330, storage component 340, input component 350, outputcomponent 360, communication interface 370, and/or the like) maymonitor, based on the monitoring information, at least one performanceindicator of the base station, as described above. When monitoring theat least one performance indicator of the base station, the dSON mayidentify, based on the monitoring information, a threshold associatedwith the at least one performance indicator and may determine whetherthe at least one performance indicator satisfies the threshold.

As further shown in FIG. 5, process 500 may include generating, based onmonitoring the at least one performance indicator, performanceinformation (block 540). For example, the dSON (e.g., using processor320, memory 330, storage component 340, input component 350, outputcomponent 360, communication interface 370, and/or the like) maygenerate, based on monitoring the at least one performance indicator,performance information, as described above. The performance informationmay include a number of times that the at least one performanceindicator satisfied a threshold, an amount of time that the at least oneperformance indicator satisfied the threshold, and/or the like.

As further shown in FIG. 5, process 500 may include sending theperformance information to the mSON (block 550). For example, the dSON(e.g., using processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370, and/orthe like) may send the performance information to the mSON, as describedabove.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

For example, the dSON may obtain, after sending the performanceinformation to the mSON, a different operating profile and a differentusage schedule, and may apply the different profile according to thedifferent usage schedule. The dSON may monitor, after applying thedifferent profile according to the different usage schedule, and basedon the monitoring information, the at least one performance indicator ofthe base station; may generate, based on monitoring the at least oneperformance indicator after applying the different profile according tothe different usage schedule, additional performance information; andmay send the additional performance information to the mSON.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 concerning a multi-tierself-organizing network architecture. In some implementations, one ormore process blocks of FIG. 6 may be performed by a cSON (e.g., cSON 108and/or cSON 240). In some implementations, one or more process blocks ofFIG. 6 may be performed by another device or a group of devices separatefrom or including the cSON, such as a device associated with a networkmanagement system of the wireless telecommunications network, and/or thelike.

As shown in FIG. 6, process 600 may include determining a cluster of awireless telecommunications network (block 610). For example, the cSON(e.g., using processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370, and/orthe like) may determine a cluster of a wireless telecommunicationsnetwork, as described above. The cluster may include one or more dSONsand one or more base stations.

As further shown in FIG. 6, process 600 may include determiningconfiguration parameters concerning the cluster (block 620). Forexample, the cSON (e.g., using processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370, and/or the like) may determine configuration parametersconcerning the cluster, as described above.

As further shown in FIG. 6, process 600 may include generating, based onthe configuration parameters, one or more operating profiles and one ormore respective usage schedules (block 630). For example, the cSON(e.g., using processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370, and/orthe like) may generate, based on the configuration parameters, one ormore operating profiles and one or more respective usage schedules, asdescribed above. The cSON may use a machine learning model to generatethe one or more operating profiles and the one or more respective usageschedules. The machine learning model may have been trained usinghistorical performance information concerning one or more dSONs.

As further shown in FIG. 6, process 600 may include sending theconfiguration parameters, the one or more operating profiles, and theone or more respective usage schedules to an mSON (block 640). Forexample, the cSON (e.g., using processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370, and/or the like) may send the configuration parameters,the one or more operating profiles, and the one or more respective usageschedules to an mSON, as described above.

As further shown in FIG. 6, process 600 may include receiving, from themSON, after sending the configuration parameters, the one or moreoperating profiles, and the one or more respective usage schedules,performance information concerning a dSON that applied an operatingprofile, of the one or more operating profiles, according to arespective usage schedule, of the one or more respective usage schedules(block 650). For example, the cSON (e.g., using processor 320, memory330, storage component 340, input component 350, output component 360,communication interface 370, and/or the like) may receive, from themSON, after sending the configuration parameters, the one or moreoperating profiles, and the one or more respective usage schedules,performance information concerning a dSON that applied an operatingprofile, of the one or more operating profiles, according to arespective usage schedule, of the one or more respective usageschedules, as described above. The dSON may update the machine learningmodel based on the performance information.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described inconnection with one or more other processes described elsewhere herein.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, more than thethreshold, higher than the threshold, greater than or equal to thethreshold, less than the threshold, fewer than the threshold, lower thanthe threshold, less than or equal to the threshold, equal to thethreshold, etc.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, it should be understood thatsuch information shall be used in accordance with all applicable lawsconcerning protection of personal information. Additionally, thecollection, storage, and use of such information can be subject toconsent of the individual to such activity, for example, through wellknown “opt-in” or “opt-out” processes as can be appropriate for thesituation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, and/or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be used to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the phrase “only one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A device associated with a middle-tierself-organizing network (mSON), comprising: one or more memories; andone or more processors, communicatively coupled to the one or morememories, to: obtain configuration parameters concerning a cluster, oneor more operating profiles, and one or more usage schedules respectivelyassociated with the one or more operating profiles, from a centralizedself-organizing network (cSON), wherein the cluster includes one or moredistributed self-organizing networks (dSONs) and one or more basestations that are associated with the one or more dSONs, and wherein theconfiguration parameters provide identification information of the oneor more dSONs, and capability information of one or more capabilities ofthe one or more base stations; identify, based on the identificationinformation, a dSON of the one or more dSONs; select, based onidentifying the dSON and the capability information, an operatingprofile of the one or more operating profiles, and a respective usageschedule of the one or more usage schedules, for the dSON, wherein thedSON is to apply the operating profile to a base station of the one ormore base stations according to the respective usage schedule to controla functionality of the base station; generate, based on theconfiguration parameters, monitoring information of the base station;and send the operating profile, the respective usage schedule, and themonitoring information to the dSON.
 2. The device of claim 1, whereinthe one or more processors are further to: receive performanceinformation from the dSON; select, based on the performance information,a different operating profile, of the one or more operating profiles,and a respective different usage schedule of the one or more usageschedules; and send the different operating profile and the respectivedifferent usage schedule to the dSON.
 3. The device of claim 2, whereinthe one or more processors are further to: send the performanceinformation to the cSON to permit the cSON to update a machine learningmodel associated with generating the one or more operating profiles andthe one or more usage schedules.
 4. The device of claim 1, wherein theone or more processors are further to: receive, from the dSON, aftersending the operating profile, the respective usage schedule, and themonitoring information to the dSON, performance information concerningthe dSON; receive, from one or more additional dSONs in the cluster,additional performance information concerning the one or more additionaldSONs; determine a performance of the cluster based on the performanceinformation concerning the dSON and the additional performanceinformation concerning the one or more additional dSONs; select, basedon the performance of the cluster, a different operating profile, of theone or more operating profiles, and a respective different usageschedule of the one or more usage schedules; and send the differentoperating profile and the respective different usage schedule to thedSON.
 5. The device of claim 1, wherein the one or more processors arefurther to: receive, from the dSON, after sending the operating profile,the respective usage schedule, and the monitoring information to thedSON, performance information concerning the dSON; and cause, based onthe performance information concerning the dSON, the dSON to shut down.6. The device of claim 1, wherein a first end-to-end transmission timefor communications between the dSON and the mSON is less than a secondend-to-end transmission time for communications between the dSON and thecSON.
 7. The device of claim 1, wherein the one or more processors, whengenerating the monitoring information, are to: identify a base stationof the one or more base stations associated with the dSON; determine atleast one capability, of the one or more capabilities, of the basestation; and generate, based on the configuration parameters, monitoringinstructions that concern the at least one capability.
 8. The device ofclaim 1, wherein the one or more processors, when selecting theoperating profile and the respective usage schedule, are to: obtaininformation concerning at least one performance indicator of the one ormore base stations; and select the operating profile and the respectiveusage schedule based on the at least one performance indicator.
 9. Thedevice of claim 8, wherein the at least one performance indicatorincludes at least one of: a data rate indicator; a connection densityindicator; a latency indicator; or a reliability indicator.
 10. Amethod, comprising: obtaining, by a distributed self-organizing network(dSON), an operating profile, a usage schedule, and monitoringinformation from a middle-tier self-organizing network (mSON), whereinthe dSON is associated with a base station, wherein the monitoringinformation provides criteria for monitoring at least one performanceindicator of the base station, and wherein the at least one performanceindicator relates to a performance of the base station in a wirelesstelecommunications network that includes the dSON and the base station;applying, by the dSON, the operating profile according to the usageschedule to control operation of the base station; identifying, by thedSON and based on the monitoring information, a performance thresholdassociated with the at least one performance indicator; determining, bythe dSON, whether the at least one performance indicator satisfies theperformance threshold; generating, by the dSON and based on determiningwhether the at least one performance indicator satisfies the threshold,performance information of the base station in the wirelesstelecommunications network, wherein the performance information includesat least one of: a number of times the at least one performanceindicator satisfied the performance threshold; or an amount of time theat least one performance indicator satisfied the performance threshold;and sending, by the dSON, the performance information to the mSON. 11.The method of claim 10, further comprising: obtaining, after sending theperformance information to the mSON, a different operating profile and adifferent usage schedule; and applying the different operating profileaccording to the different usage schedule.
 12. The method of claim 11,further comprising: monitoring, after applying the different operatingprofile according to the different usage schedule and based on themonitoring information, the at least one performance indicator of thebase station; generating, by the dSON and based on the monitoring of theat least one performance indicator after applying the differentoperating profile according to the different usage schedule, anadditional performance information; and sending, by the dSON, theadditional performance information to the mSON.
 13. The method of claim10, wherein the operating profile is at least one of: an enhanced mobilebroad band profile; a massive machine-type communication profile; aconnected vehicles profile; an enhanced multi-media profile; an internetof things profile; an ultra-reliable low latency communication profile;or a fixed wireless access profile.
 14. The method of claim 10, whereinapplying the operating profile according to the usage schedulecomprises: determining, based on the usage schedule, a period of time inwhich to apply the operating profile; and causing the base station tooperate according to the operating profile during the period of time.15. The method of claim 10, wherein the at least one performanceindicator includes at least one of: a data rate indicator; a connectiondensity indicator; a latency indicator; or a reliability indicator. 16.A non-transitory computer-readable medium storing instructions, theinstructions comprising: one or more instructions that, when executed byone or more processors associated with a centralized self-organizingnetwork (cSON), cause the one or more processors to: determine a clusterof a wireless telecommunications network, wherein the cluster includesone or more distributed self-organizing networks (dSONs) and one or morebase stations; determine configuration parameters concerning thecluster, wherein the configuration parameters include one or moreoperational requirements supported by at least one of the one or moredSONs or the one or more base stations; generate, based on the one ormore operational requirements, one or more operating profiles and one ormore respective usage schedules; send the configuration parameters, theone or more operating profiles, and the one or more respective usageschedules to a middle-tier self-organizing network (mSON); and receive,from the mSON, after sending the configuration parameters, the one ormore operating profiles, and the one or more respective usage schedules,performance information concerning a dSON, of the one or more dSONs,that applied an operating profile, of the one or more operatingprofiles, according to a respective usage schedule, of the one or morerespective usage schedules.
 17. The non-transitory computer-readablemedium of claim 16, wherein the one or more instructions, that cause theone or more processors to generate the one or more operating profilesand the one or more respective usage schedules, cause the one or moreprocessors to: generate, using a machine learning model, the one or moreoperating profiles and the one or more respective usage schedules,wherein the machine learning model has been trained using historicalperformance information concerning the one or more dSONs.
 18. Thenon-transitory computer-readable medium of claim 17, wherein the one ormore instructions, when executed by the one or more processors, furthercause the one or more processors to: update the machine learning modelbased on the received performance information concerning the dSON. 19.The non-transitory computer-readable medium of claim 16, wherein the oneor more operating profiles include one or more characteristics forsupporting one or more particular types of communication between the oneor more dSONs and the one or more base stations.
 20. The non-transitorycomputer-readable medium of claim 16, wherein the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: generate, based on the performanceinformation, one or more instructions to control the one or more basestations.